Artificial Intelligence (AI) has rapidly evolved to provide answers to complex questions, but smart business leaders understand that decisions shouldn't rely solely on the answers themselves. Instead, they ask critical questions: Where did the information come from? What assumptions were made in reaching the conclusion? And how reliable is the recommendation? These considerations are becoming more vital as AI plays a larger role in shaping enterprise decisions. From marketing teams evaluating campaign ideas to insights teams analyzing years of consumer data, AI is increasingly used to support decision-making in areas that impact business strategy and growth. When AI begins to influence major decisions rather than just speed up tasks, understanding its reasoning becomes just as important as the outcome. For example, a consumer packaged goods (CPG) company considering expansion into convenience stores while maintaining its presence in supermarkets must weigh a variety of factors, such as the impact on supermarket sales, customer demographics, and long-term brand strategy. No single report contains all this information, and leaders must piece together insights from multiple sources. AI can help by quickly synthesizing research, identifying patterns, and presenting findings in a digestible format, allowing teams to focus more on evaluation than data collection. However, AI does not replace the need for human judgment. Leaders must still understand the reasoning behind AI-generated recommendations. Many current AI tools provide compelling answers but often lack transparency about how those conclusions were reached. These tools combine various data sources, including proprietary research, web content, and AI-generated material, without clearly indicating how each component contributed to the final recommendation. As a result, AI systems often act like "black boxes," offering recommendations without the necessary context. While this may be acceptable for less critical tasks, it is insufficient for high-stakes decisions involving large investments, new products, or strategic planning. A key challenge in using AI for decision-making is understanding the certainty behind its conclusions. Some view any uncertainty in AI's output as a flaw rather than a feature. However, uncertainty is a natural part of good decision-making. Experienced leaders understand that perfect information is rarely available, and they must weigh where the evidence is strong and where it is limited. Traditional research methods naturally encouraged such discussions, but AI can streamline the process to the point where uncertainties are overlooked. Recognizing gaps in evidence or conflicting findings can lead to better questions, more research, and more informed decisions. To address these challenges, a "Glass Box" approach to AI is emerging—one that emphasizes transparency throughout the AI decision-making process. This approach ensures that every AI output includes an explanation of its reasoning, allowing leaders to understand the evidence, assumptions, and filters used. Rather than simply citing a large body of documents, AI should reference specific pages or passages from source materials. It should clearly distinguish between what the source material stated and what was inferred by the AI. Additionally, it should highlight areas with weak evidence or missing information, allowing users to consider these factors in their decision-making. This approach also allows for user intervention. Leaders should be able to question, challenge, or adjust AI-generated conclusions and document their reasoning. For instance, if AI recommends that a CPG firm can charge higher prices in convenience stores, but a product team member disagrees, that disagreement should be recorded and considered. Transparency should be maintained throughout the AI's processing cycle, not just at the end. This ensures that users can identify and correct issues early, leading to more reliable and well-informed decisions. As organizations increasingly rely on AI for strategic decisions, the need for traceable, transparent, and challengeable AI outputs has never been more critical. "Trust me" is no longer an acceptable justification for major business decisions. Instead, enterprises require clear evidence, logical reasoning, and conclusions that can withstand scrutiny. The "Glass Box" approach to AI represents a step toward meeting this new standard, ensuring that the value of AI is measured not by how confidently it answers, but by how confidently organizations can act on those answers.